Diving behaviour of narwhals is still largely unknown. We use Hidden Markov models (HMMs) to describe the diving behaviour of a narwhal and fit the models to a three-dimensional response vector of maximum dive depth, duration of dives and post-dive surface time of 8,609 dives measured in East Greenland over 83 days, an extraordinarily long and rich data set. Narwhal diving patterns have not been analysed like this before, but in studies of other whale species, response variables have been assumed independent. We extend the existing models to allow for dependence between state distributions, and show that the dependence has an impact on the conclusions drawn about the diving behaviour. We try several HMMs with 2, 3 or 4 states, and with independent and dependent log-normal and gamma distributions, respectively, and different covariates to characterize dive patterns. In particular, diurnal patterns in diving behaviour is inferred, by using periodic B-splines with boundary knots in 0 and 24 hours.
2019 / Ditlevsen, S. and A.Samson
Hypoelliptic diffusions: discretization, filtering and inference from complete and partial observations
Journal of the Royal Statistical Society, series B, 81(2), 361-384
The statistical problem of parameter estimation in partially observed hypoelliptic diffusion processes is naturally occurring in many applications. However, due to the noise structure, where the noise components of the different coordinates of the multi-dimensional process operate on different time scales, standard inference tools are ill conditioned. In this paper, we propose to use a higher order scheme to discretize the process and approximate the likelihood, such that the different time scales are appropriately accounted for. We show consistency and asymptotic normality with non-typical convergence rates. When only partial observations are available, we embed the approximation into a filtering algorithm for the unobserved coordinates, and use this as a building block in a Stochastic Approximation Expectation Maximization algorithm. We illustrate on simulated data from three models; the Harmonic Oscillator, the FitzHugh-Nagumo model used to model the membrane potential evolution in neuroscience, and the Synaptic Inhibition and Excitation model used for determination of neuronal synaptic input.
2019 / Gualdi, S. and A. Mandel
Endogenous growth in production networks
Journal of Evolutionary Economics, Vol. 29(1), 91-117
We investigate the interplay between technological change and macro- economic dynamics in an agent-based model of the formation of production networks. On the one hand, production networks form the structure that determines economic dynamics in the short run. On the other hand, their evolution reflects the long-term impacts of competition and innovation on the economy. We account for process innovation via increasing variety in the input mix and hence increasing connectivity in the network. In turn, product innovation induces a direct growth of the firm’s productivity and the potential destruction of links. The interplay between both processes generates complex technological dynamics in which phases of process and product innovation successively dominate. The model reproduces a wealth of stylized facts about industrial dynamics and technological progress, in particular the persistence of heterogeneity among firms and Wright’s law for the growth of productivity within a technological paradigm. We illustrate the potential of the model for the analysis of industrial policy via a preliminary set of policy experiments in which we investigate the impact on innovators’ success of feed-in tariffs and of priority market access.
2019 / Grabisch, M., A. Poindron and A. Rusinowska
A model of anonymous influence with anti-conformist agents
Journal of Economic Dynamics and Control, Vol. 109, Art.-Nr. 103773
We study a stochastic model of anonymous influence with conformist and anti-conformist individuals. Each agent with a ‘yes’ or ‘no’ initial opinion on a certain issue can change his opinion due to social influence. We consider anonymous influence, which depends on the number of agents having a certain opinion, but not on their identity. An individual is conformist/anti-conformist if his probability of saying ‘yes’ increases/decreases with the number of ‘yes’-agents. We focus on three classes of aggregation rules (pure conformism, pure anti-conformism, and mixed aggregation rules) and examine two types of society (without, and with mixed agents). For both types we provide a complete qualitative analysis of convergence, i.e., identify all absorbing classes and conditions for their occurrence. Also the pure case with infinitely many individuals is studied. We show that, as expected, the presence of anti-conformists in a society brings polarization and instability: polarization in two groups, fuzzy polarization (i.e., with blurred frontiers), cycles, periodic classes, as well as more or less chaotic situations where at any time step the set of ‘yes’-agents can be any subset of the society. Surprisingly, the presence of anti-conformists may also lead to opinion reversal: a majority group of conformists with a stable opinion can evolve by a cascade phenomenon towards the opposite opinion, and remains in this state.
We study a model where agents face a continuum of two-player games and categorize them into a finite number of situations to make sense of their complex environment. Agents need not share the same categorization. Each agent can cooperate or defect, conditional on the perceived category. The games are fully ordered by the strength of the temptation to defect and break joint cooperation. In equilibrium agents share the same categorization, but achieve less cooperation than if they could perfectly discriminate games. All the equilibria are evolutionarily stable, but stochastic stability selects against cooperation. We model agents’ learning when they imitate successful players over similar games, but lack any information about the opponents’ categorizations. We show that imitation conditional on reaching an intermediate aspiration level leads to a shared categorization that achieves higher cooperation than under perfect discrimination.
2018 / Dawid, H., P. Harting and M. Neugart
Cohesion Policy and Inequality Dynamics: Insights from a Heterogeneous Agents Macroeconomic Model
Journal of Economic Behavior and Organization, Vol. 150, 220-255
Regions within the European Union differ substantially not only with respect to per capita GDP, but also with respect to income inequality within the regions. This paper studies the effects of different types of technology-oriented cohesion policies, aiming at the reduction of regional differences, on the convergence of regions and the dynamics of income inequality within regions. In particular, policies are analyzed using a two-region agent-based macroeconomic model – the Eurace@Unibi model – where firms in the lagging region receive subsidies for investment in physical capital. It is demonstrated that the short-, medium- and long-term effects of the policies on per-capita output and between- as well as within-regional inequality differ substantially. Effects depend on how successful the policy is in incentivizing firms to choose best available capital vintages and on how flexible labor markets are in the targeted region.
2018 / Quax R., G. Chliamovitch, A. Dupuis, J.-L. Falcone , B. Chopard, A.G. Hoekstra and P. M. A. Sloot
Information processing features can detect behavioral regimes of dynamical systems
In dynamical systems, local interactions between dynamical units generate correlations which are stored and transmitted throughout the system, generating the macroscopic behavior. However a framework to quantify exactly how these correlations are stored, transmitted, and combined at the microscopic scale is missing. Here we propose to characterize the notion of “information processing” based on all possible Shannon mutual information quantities between a future state and all possible sets of initial states. We apply it to the 256 elementary cellular automata (ECA), which are the simplest possible dynamical systems exhibiting behaviors ranging from simple to complex. Our main finding is that only a few information features are needed for full predictability of the systemic behavior and that the “information synergy” feature is always most predictive. Finally we apply the idea to foreign exchange (FX) and interest-rate swap (IRS) time-series data. We find an effective “slowing down” leading indicator in all three markets for the 2008 financial crisis when applied to the information features, as opposed to using the data itself directly. Our work suggests that the proposed characterization of the local information processing of units may be a promising direction for predicting emergent systemic behaviors.
2018 / Halleck-Vega, S., A. Mandel and K. Millock
Accelerating diffusion of climate-friendly technologies: A network perspective
We introduce a methodology to estimate the determinants of the formation of technology diffusion networks from the patterns of technology adoption. We apply this methodology to wind energy, which is one of the key technologies in climate change mitigation. Our results emphasize that, in particular, long-term relationships as measured by economic integration are key determinants of technological diffusion. Specific support measures are less relevant, at least to explain the extensive margin of diffusion. Our results also highlight that the scope of technological diffusion is much broader than what is suggested by the consideration of CDM projects alone, which are particularly focused on China and India. Finally, the network of technological diffusion inferred from our approach highlights the central role of European countries in the diffusion process and the absence of large hubs among developing countries.
2018 / Conti C., M.L. Mancusi, R. Sestini, F. Sanna Randaccio and E. Verdolini
Transition Towards a Green Economy in Europe: Innovation and Knowledge Integration in the Renewable Energy Sector
This paper investigates the fragmentation of the EU innovation system in the field of renewable energy sources (RES) by estimating the intensity and direction of knowledge spillovers over the years 1985–2010. We modify the original double exponential knowledge diffusion model proposed by Caballero and Jaffe (1993) to provide information on the degree of integration of EU countries’ RES knowledge bases and to assess how citation patterns changed over time. We show that EU RES inventors have increasingly built “on the shoulders of the other EU giants”, intensifying their citations to other member countries and decreasing those to domestic inventors. Furthermore, the EU strengthened its position as source of RES knowledge for the US. Finally, we show that this pattern is peculiar to RES, with other traditional (i.e. fossil-based) energy technologies and other radically new technologies behaving differently. Our results provide suggestive, but convincing evidence that the reduction in fragmentation emerged as a result of the EU support for RES taking mainly the form of demand-pull policies.
2018 / Moro, A. and P. Pellizzari
A computational model of labor market participation with health shocks and bounded rationality
Knowledge and Information Systems, Vol. 54, 617–631
This paper presents a computational agent-based model of labor market participation, in which a population of agents, affected by adverse health shocks that impact the costs associated with working efforts, decides whether to leave the labor market and retire. This decision is simply taken by looking at the working behaviors of the other agents, comparing the respective levels of well-being and imitating the more advantageous decision of others. The analysis reveals that such mechanism of social learning and imitation suffices to replicate the existing empirical evidence regarding the decline in labor market participation of older people. As a consequence, the paper demonstrates that it is not necessary to assume perfect and unrealistic rationality at the individual level to reproduce a rational behavior in the aggregate.
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